Cognitive file and object management for distributed storage environments

ABSTRACT

In one embodiment, a method includes filtering a plurality of files stored to a central cluster of a distributed file system to place independent portions of the plurality of files into a plurality of groups using filters prior to receiving a query on the plurality of files. Files within each of the plurality of groups share a common searchable characteristic. The method also includes receiving, at the central cluster, an indication of the query. Moreover, the method includes responding to the query by duplicating files of one or more of the plurality of groups that correspond to the query to a local cluster of the distributed file system that provided the indication of the query and is geographically diverse from the central cluster. Other methods, systems, and computer program products for cognitive data management are described in accordance with more embodiments.

BACKGROUND

The present invention relates to distributed storage environments, andmore particularly, to file and object management in distributed storageenvironments using cognitive techniques.

A central distributed clustered file system is able to provide a globalnamespace for all files and/or objects stored within the distributedfile system. In effect, such as system operates as a global datarepository, and is able to include massive amounts of data storagecapacity and may be tiered across various storage classes. Such assystem includes a central data repository cluster, and a plurality oflocal clusters smaller in size than the central data repository. Eachlocal cluster, also referred to as a cache cluster, includes a pluralityof storage devices, of one or more storage classes, and may utilize widearea network (WAN) caching techniques to cache portions of the datastored to the distributed file system within a cache of the localcluster.

Typically, WAN caching is used to more efficiently provide data that isfrequently accessed by a user on the local cluster. In this way, queriesand/or analytic operations that are executed on the local cluster arerun against the local cluster. Storing all data at each local clusterand on the central cluster is impractical due to the high data storagerequirements and data synchronization costs. Therefore, the centralcluster is often used to store a global copy of data from which thevarious local clusters can retrieve the data for queries and/or analyticoperations. In this typical usage model, data is received and ingestedat the various local clusters, which may be geographically spread acrossthe physical footprint of the distributed file system. The ingested datafrom the various local clusters is subsequently duplicated to thecentral cluster to be stored as a global copy. In an alternate usagemodel, the central cluster may serve as a data repository and multiplelocal clusters may utilize data stored to the central repository asread-only instances for faster access thereto.

In either usage model, latency for remote access of data stored to thecentral cluster is higher than desired for users of the distributed filesystem, unless the data is also stored at the local cluster from whichthe user is attempting access. This causes fluctuations in accessperformance for the distributed file system.

Moreover, big data analytics may require scanning very large portions(or all) of the data stored to the global data repository. This may betoo resource intensive to execute all queries directly on the globaldata repository, and therefore queries may be executed on a device otherthan the controller that has access to the global data repository bycopying over all the data from the global data repository to the otherdevice. In some instances, after running the analytics operation(s), thecopied-over data is deleted from the other device, which then requirescopying over all the data from the global data repository again forexecution of a subsequent analytics operation. In other instances, thecopied-over data may be cached, which requires an enormous amount oflocal storage on the other device. In either instance, the copied-overdata includes all of the data in the global data repository, as there isno intelligence that determines what data is to be used in the analyticsoperation and what may remain uncopied on the global data repository.Moreover, not all of the copied-over data may be used in the analyticsoperation and is just dropped in processing the query, thus wastingconsiderable resources, such as network bandwidth, processing bandwidth,memory capacity, time, etc.

Alternatively, when there is sufficient processing capacity in theglobal data repository to process all queries, the data may still becached (i.e., copied over and preserved in local memory to the globaldata repository) in order to speed up query processing. Similar resourcecosts apply when executing the queries in this fashion as when copyingover the data to execute the queries on the other device.

SUMMARY

In one embodiment, a method includes filtering a plurality of filesstored to a central cluster of a distributed file system to placeindependent portions of the plurality of files into a plurality ofgroups using filters prior to receiving a query on the plurality offiles. Files within each of the plurality of groups share a commonsearchable characteristic. The method also includes receiving, at thecentral cluster, an indication of the query. Moreover, the methodincludes responding to the query by duplicating files of one or more ofthe plurality of groups that correspond to the query to a local clusterof the distributed file system that provided the indication of the queryand is geographically diverse from the central cluster.

In another embodiment, a computer program product includes a computerreadable storage medium having program instructions embodied therewith.The computer readable storage medium is not a transitory signal per se,and the embodied program instructions are executable by a processingcircuit to cause the processing circuit to filter, by the processingcircuit, a plurality of files stored to a central cluster of adistributed file system to place independent portions of the pluralityof files into a plurality of groups using filters prior to receiving aquery on the plurality of files. Files within each of the plurality ofgroups share a common searchable characteristic. Also, the embodiedprogram instructions are executable by the processing circuit to causethe processing circuit to receive, by the processing circuit at thecentral cluster, an indication of the query. Moreover, the embodiedprogram instructions are executable by the processing circuit to causethe processing circuit to respond to the query, by the processingcircuit, by duplicating files of one or more of the plurality of groupsthat correspond to the query to a local cluster of the distributed filesystem that provided the indication of the query.

In yet another embodiment, a system includes a processing circuit, amemory, and logic stored to the memory, that when executed by theprocessing circuit causes the processing circuit to filter a pluralityof files stored to a central cluster of a distributed file system toplace independent portions of the plurality of files into a plurality ofgroups using filters prior to receiving a query on the plurality offiles. Files within each of the plurality of groups share a commonsearchable characteristic. The logic when executed by the processingcircuit also causes the processing circuit to receive, at the centralcluster, an indication of the query. Moreover, the logic when executedby the processing circuit causes the processing circuit to respond tothe query by duplicating files of one or more of the plurality of groupsthat correspond to the query to a local cluster of the distributed filesystem that provided the indication of the query.

According to another embodiment, a method includes receiving a pluralityof files at a central cluster of a distributed file system from one ormore sources, the plurality of files including text and unstructureddata. Also, the method includes storing the plurality of files to thecentral cluster and converting the unstructured data into text on thecentral cluster. Moreover, the method includes filtering the pluralityof files to place independent portions of the plurality of files into aplurality of groups using filters prior to receiving a query on theplurality of files. Files within each of the plurality of groups share acommon searchable characteristic, and the filters are applied to thetext of the files after being converted from the unstructured data.

According to yet another embodiment, a method includes searching a localcluster of a distributed file system for files relevant to a query priorto sending an indication of the query to a central cluster of thedistributed file system. The method also includes sending the indicationof the query to the central cluster in response to a determination thatthe relevant files are not stored to the local cluster. Also, the methodincludes receiving, at the local cluster, a group of files relevant tothe query. In addition, the method includes executing the query on thegroup of files, with the proviso that the group of files does notinclude all files stored to the central cluster. Moreover, the methodincludes storing the group of files on the local cluster for apredetermined period of time starting from a last access of the group offiles in accordance with a cache eviction policy.

Other aspects and embodiments of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 illustrates a tiered data storage system in accordance with oneembodiment.

FIGS. 5A-5C show a distributed system during several stages of filteringand grouping data for efficient query handling, according to oneembodiment.

FIG. 6 shows data filtering and grouping for efficient query handling inan exemplary distributed system.

FIG. 7 shows a flowchart of a method, according to one embodiment.

FIG. 8 shows a flowchart of a method, according to one embodiment.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. The term“about” as used herein indicates the value preceded by the term “about,”along with any values reasonably close to the value preceded by the term“about,” as would be understood by one of skill in the art. When notindicated otherwise, the term “about” denotes the value preceded by theterm “about”±10% of the value. For example, “about 10” indicates allvalues from and including 9.0 to 11.0.

The following description discloses several preferred embodiments ofsystems, methods, and computer program products for cognitive managementof files and objects in a distributed storage environment.

In one general embodiment, a method includes filtering a plurality offiles stored to a central cluster of a distributed file system to placeindependent portions of the plurality of files into a plurality ofgroups using filters prior to receiving a query on the plurality offiles. Files within each of the plurality of groups share a commonsearchable characteristic. The method also includes receiving, at thecentral cluster, an indication of the query. Moreover, the methodincludes responding to the query by duplicating files of one or more ofthe plurality of groups that correspond to the query to a local clusterof the distributed file system that provided the indication of the queryand is geographically diverse from the central cluster.

In another general embodiment, a computer program product includes acomputer readable storage medium having program instructions embodiedtherewith. The computer readable storage medium is not a transitorysignal per se, and the embodied program instructions are executable by aprocessing circuit to cause the processing circuit to filter, by theprocessing circuit, a plurality of files stored to a central cluster ofa distributed file system to place independent portions of the pluralityof files into a plurality of groups using filters prior to receiving aquery on the plurality of files. Files within each of the plurality ofgroups share a common searchable characteristic. Also, the embodiedprogram instructions are executable by the processing circuit to causethe processing circuit to receive, by the processing circuit at thecentral cluster, an indication of the query. Moreover, the embodiedprogram instructions are executable by the processing circuit to causethe processing circuit to respond to the query, by the processingcircuit, by duplicating files of one or more of the plurality of groupsthat correspond to the query to a local cluster of the distributed filesystem that provided the indication of the query.

In yet another general embodiment, a system includes a processingcircuit, a memory, and logic stored to the memory, that when executed bythe processing circuit causes the processing circuit to filter aplurality of files stored to a central cluster of a distributed filesystem to place independent portions of the plurality of files into aplurality of groups using filters prior to receiving a query on theplurality of files. Files within each of the plurality of groups share acommon searchable characteristic. The logic when executed by theprocessing circuit also causes the processing circuit to receive, at thecentral cluster, an indication of the query. Moreover, the logic whenexecuted by the processing circuit causes the processing circuit torespond to the query by duplicating files of one or more of theplurality of groups that correspond to the query to a local cluster ofthe distributed file system that provided the indication of the query.

According to another general embodiment, a method includes receiving aplurality of files at a central cluster of a distributed file systemfrom one or more sources, the plurality of files including text andunstructured data. Also, the method includes storing the plurality offiles to the central cluster and converting the unstructured data intotext on the central cluster. Moreover, the method includes filtering theplurality of files to place independent portions of the plurality offiles into a plurality of groups using filters prior to receiving aquery on the plurality of files. Files within each of the plurality ofgroups share a common searchable characteristic, and the filters areapplied to the text of the files after being converted from theunstructured data.

According to yet another general embodiment, a method includes searchinga local cluster of a distributed file system for files relevant to aquery prior to sending an indication of the query to a central clusterof the distributed file system. The method also includes sending theindication of the query to the central cluster in response to adetermination that the relevant files are not stored to the localcluster. Also, the method includes receiving, at the local cluster, agroup of files relevant to the query. In addition, the method includesexecuting the query on the group of files, with the proviso that thegroup of files does not include all files stored to the central cluster.Moreover, the method includes storing the group of files on the localcluster for a predetermined period of time starting from a last accessof the group of files in accordance with a cache eviction policy.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and cognitive file/object management fordistributed storage environments 96.

Now referring to FIG. 4, a tiered storage system 400 is shown accordingto one embodiment, which may be representative of a public tiered objectstore in some approaches. Note that some of the elements shown in FIG. 4may be implemented as hardware and/or software, according to variousembodiments. The storage system 400 may include a storage system manager412 for communicating with a plurality of media on at least one higherstorage tier 402 and at least one lower storage tier 406. The higherstorage tier(s) 402 preferably may include one or more random accessand/or direct access media 404, such as nonvolatile memory (NVM), solidstate memory in solid state drives (SSDs), flash memory, SSD arrays,flash memory arrays, hard disks in hard disk drives (HDDs), etc., and/orothers noted herein or known in the art. The lower storage tier(s) 406may preferably include one or more lower performing storage media 408,including slower accessing HDDs, sequential access media such asmagnetic tape in tape drives and/or optical media, etc., and/or othersnoted herein or known in the art. One or more additional storage tiers416 may include any combination of storage memory media as desired by adesigner of the system 400. Also, any of the higher storage tiers 402and/or the lower storage tiers 406 may include some combination ofstorage devices and/or storage media.

The storage system manager 412 may communicate with the storage media404, 408 on the higher storage tier(s) 402 and lower storage tier(s) 406through a network 410, such as a storage area network (SAN), as shown inFIG. 4, or some other suitable network type. The storage system manager412 may also communicate with one or more host systems (not shown)through a host interface 414, which may or may not be a part of thestorage system manager 412. The storage system manager 412 and/or anyother component of the storage system 400 may be implemented in hardwareand/or software, and may make use of a processor (not shown) forexecuting commands of a type known in the art, such as a centralprocessing unit (CPU), a field programmable gate array (FPGA), anapplication specific integrated circuit (ASIC), etc. Of course, anyarrangement of a storage system may be used, as will be apparent tothose of skill in the art upon reading the present description.

In more embodiments, the storage system 400 may include any number ofdata storage tiers, and may include the same or different storage memorymedia within each storage tier. For example, each data storage tier mayinclude the same type of storage memory media, such as HDDs, SSDs,sequential access media (tape in tape drives, optical disk in opticaldisk drives, etc.), direct access media (CD-ROM, DVD-ROM, etc.), or anycombination of media storage types. In one such configuration, a higherstorage tier 402, may include a majority of SSD storage media (up to andincluding all SSD storage media) for storing data in a higher performingstorage environment, and remaining storage tiers, including lowerstorage tier 406 and additional storage tiers 416 may include anycombination of SSDs, HDDs, tape drives, etc., for storing data in alower performing storage environment. In this way, more frequentlyaccessed data, data having a higher priority, data needing to beaccessed more quickly, etc., may be stored to the higher storage tier402, while data not having one of these attributes may be stored to theadditional storage tiers 416, including lower storage tier 406. Ofcourse, one of skill in the art, upon reading the present descriptions,may devise many other combinations of storage media types to implementinto different storage schemes, according to the embodiments presentedherein.

In one particular embodiment, the storage system 400 may include acombination of SSDs and HDDs, with the higher storage tier 402 includingSSDs (and possibly some buffer memory) and the lower storage tier 406including HDDs (and possibly some buffer memory). According to anotherembodiment, the storage system 400 may include a combination of SSDs andmagnetic tape with magnetic tape drives, with the higher storage tier402 including SSDs (and possibly some buffer memory) and the lowerstorage tier 406 including magnetic tape (and possibly some buffermemory) and magnetic tape drives for accessing data from the magnetictapes. In yet another embodiment, the storage system 400 may include acombination of HDDs and magnetic tape, with the higher storage tier 402including HDDs (and possibly some buffer memory) and the lower storagetier 406 including magnetic tape (and possibly some buffer memory).

Now referring to FIG. 5A, a block diagram of a central, distributed, andclustered file system 500 (hereafter “distributed system 500”) is shownaccording to one embodiment. The distributed system 500 may include anynumber of files and/or objects (hereafter “files 504”) that store and/orinclude information and/or data, and are accessible to one of more users502 of the distributed system 500. Moreover, the distributed system 500includes a central cluster 506 that is configured as a global datarepository, a plurality of local cache clusters 508 a, 508 b, . . . ,508 n (hereafter “local clusters 508” when referred to as a group) thatare geographically diverse from the central cluster, and one or morenetworks 510 that couple the various local clusters 508 with the centralcluster 506. Any type of network(s) 510 may be used, as would beapparent to one of skill in the art upon reading the presentdescriptions, such as, but not limited to, the Internet, a WAN, a LAN, aSAN, etc.

In addition, the central cluster 506 and each of the local clusters 508include a hardware processing circuit configured to execute programinstructions provided thereto. Other hardware and/or software componentsnot specifically described herein may also be included in the centralcluster 506 and/or one or more of the local clusters 508 as would beknown to one of skill in the art.

In one embodiment, one or more of the hardware components within thecentral cluster 506 and/or one or more of the local clusters 508 mayhave redundant components installed in parallel in order to performredundant functionality in cases where a primary hardware componentfails, loses power, etc., and is unavailable to perform its assignedtask(s).

In addition, the central cluster 506 and each of the local clusters 508include one or more types of computer readable storage media 512. Anytype of computer readable storage media 512 may be utilized in thecentral cluster 506 and the various local clusters 508, such as but notlimited to, non-volatile memory (NVM) storage devices, direct accessstorage devices (DASDs), random access memory units, etc. Any suitableNVM storage device(s) may be utilized, such as Flash memory, RAM,erasable programmable read-only memory (EPROM), solid state devices(SSDs), etc. Moreover, any DASDs may be used, such as HDDs, tape mediafor use with a tape drive, optical drives, etc. Additionally, a cache orbuffer may also be present in the computer readable storage media 512for data staging prior to storage on the computer readable storage media512.

The files 504 stored to the central cluster 506 and files stored to thelocal clusters 508 may include information that varies in type (textdata, video data, audio data, unstructured data, etc.), size, substanceor content, etc., as would be understood by one of skill in the art.Moreover, metadata associated with the files 504 may indicate at leastsome of the characteristics of the various files 504 in some approaches.However, the files 504 stored to the central cluster 506 are notorganized in any meaningful way that provides for efficient searchingthereof, in conventional approaches.

Now referring to FIG. 5B, a process is described that provides forcognitive filtering of the files 504 into sub-containers on the centralcluster 506 according to one embodiment. This process improves andenhances the ability to search and/or filter the files 504 stored to thecentral cluster 506 to determine files more likely to be relevant to aquery, in various approaches.

Any known search algorithm may be used, such as but not limited to theRabin-Karp string search algorithm, finite-state automaton-based searchalgorithm, Knuth-Morris-Pratt algorithm, etc., along with proprietaryalgorithms provided by major technology companies, such as Google®,Microsoft®, Baidu®, Tencent®, etc.

In order to organize the files 504 on the central cluster 506, allunstructured data stored in the files 504 is converted into text intext-based or text-annotated files (in the case of images and/or videofiles, text annotations may be added to the original files as metadata),which are readily and efficiently searchable using any of a plurality ofconventional search algorithms. For the remainder of these descriptions,pure text files and mixed content text-annotated files will be referredto as text-based files. Once an original file from the files 504 isconverted into a text-based file, an association is made between theoriginal file and the produced text-based file so that any search thatreturns the text-based file is able to be traced back to the originalfile.

In one embodiment, one or more application program interfaces (APIs) maybe used to convert the unstructured data into text-based data. Each typeof unstructured data may have a different API applied thereto in orderto convert the unstructured data into text-based data. In oneembodiment, IBM® BlueMix® Watson APIs may be used for the conversion totext-based data.

According to one embodiment, all files 504 stored on the central cluster506 may be searched using one or more search algorithms that aredesigned to search particular content or particular unstructured datawithin the files 504. In one example, an image search algorithm may beconfigured to specifically search image files and return image(s) thatare specified in the search such that each of the files which includeimage data may be searched using this search algorithm, and those fileswhich include the specified image(s) will be returned by the imagesearch. In a further example, files which do not include image data willnot have this image search algorithm applied thereto.

In another example, an audio search algorithm may be configured tospecifically search audio files and return audio data that is specifiedin the search such that each of the files which include audio data maybe searched using this search algorithm, and those files which includethe specified audio data will be returned by the audio search. In afurther example, files which do not include audio data will not havethis audio search algorithm applied thereto.

Of course, many other content-specific and/or unstructured data searchalgorithms, including those designed to search custom and/or proprietaryunstructured data forms, may be used in searching and organizing thefiles 504 stored on the central cluster 506 in various additionalembodiments, as would be apparent to one of skill in the art uponreading the present descriptions.

The text-based files and/or all files 504 stored on the central cluster506 are analyzed to determine one or more relevant categories for eachof the text-based files and/or all files 504 stored on the centralcluster 506 so that filtering and grouping may be performed on thevarious files 504 (and ultimately the associated files). The relevancyof the categories is selected based on interests of a particular user,e.g., a type of business the user is engaged in, a geographic location(e.g., of the user, a home, a place of business, etc.), a date of thequery, etc.

In various non-limiting examples, the text-based files and/or all files504 stored on the central cluster 506 may be filtered and groupedaccording to a date associated with the individual files, a geographicallocation mentioned in and/or associated with the individual files, ageographical location of creation of the individual files, similarand/or common content of the individual files (which may be based onkeyword(s) within the individual files), similar usage of the individualfiles, a frequency of access to the individual files, etc.

In another non-limiting example, the text-based files and/or all files504 stored on the central cluster 506 may be filtered and groupedaccording to a value, multiple values, and/or a range of values that arestored within the individual files, with the values relating to aspecified parameter in the query, such as date, currency, time, virtualor physical location, user group(s), access privilege, or some otherspecified value of interest to the creator of the query. The specifiedvalue of interest may be anything that is of importance related to abusiness, education, pursuit, and/or interest for which data is storedin the distributed storage system. In several examples, for a medicalindustry application, a specified value of interest may be one or morepatient categorizations (e.g., age, gender, race, existing conditions,etc.), one or more condition categorizations (e.g., cancer, asthma,strep throat, arthritis, etc.), one or more test categorizations (e.g.,radiological testing, genetic testing, physical examination, etc.),etc.; for a financial industry application, a specified value ofinterest may be one or more ticker symbols (e.g., MSFT, INTL, T, etc.),one or more industry categorizations (e.g., technology, software,pharmaceutical, manufacturing, etc.), one or more valuation metrics(e.g., over 1 billion capitalization, small-, mid-, large-cap, etc.),etc.; for a pharmacological industry application, a specified value ofinterest may be one or more drug-based categorizations (e.g., statin,caffeine, benzodiazepine, fentanyl, acetaminophen, morphine, opiate,oxycodone, etc.), one or more scientists associated with a treatment,one or more treatment applications (pain-reliever, swelling reducer,local anesthetic, etc.), one or more dosages, etc.

As one of skill in the art would appreciate upon reading the presentdescriptions, a specific value of interest(s) may be custom definedbased on a particular application and the specific queries that willutilize the specific value(s) of interest, in various embodiments.

In yet another non-limiting example, the text-based files and/or allfiles 504 stored on the central cluster 506 may be filtered and groupedaccording to a popularity of the information included in the individualfiles as measured by the central cluster 506 and how often anyparticular file is duplicated onto one of the local clusters 508.

According to another embodiment, sub-groups may be created within agroup, and multiple additional levels of sub-groups may exist within aparticular group in a tree structure. In this way, files that are sortedinto the particular group may be further sorted into sub-groups that areeven more refined than the particular group. For example, when groupingby location, country may populate the top groups, followed by sub-groupsfor states or provinces, then by sub-groups for cities, followed bysub-groups for communities within cities, etc.

In one embodiment, this filtering and grouping operation may be executedas a continuous background process executed on the central cluster 506,such that as new files are added to the central cluster 506, they areable to be converted (if necessary) and categorized efficiently and withlittle effect on other functionality of the distributed system 500. Inanother embodiment, the filtering and grouping operation may be executedperiodically or in response to a triggering event taking place. Anysuitable triggering event may be used, such as new file(s) being addedto the central cluster 506, file(s) being modified on the centralcluster 506, a predetermined threshold amount of files being added toand/or modified on the central cluster 506, an explicit request from anadministrator, etc.

In one embodiment, the text-based files and/or all files 504 stored onthe central cluster 506 may be filtered and grouped according to one ormore keywords. The keyword(s) may be automatically created based oninput from a number of users interacting with the various local clusters508 and may represent most commonly queried terms over a predeterminedperiod of time.

When a query is executed on a local cluster, the query or someindication of the query is typically sent to the central cluster 506 todetermine which of the files 504 satisfy the query. The indication ofthe query may be the query itself, a set of queries previously receivedand/or anticipated to be received in the future, and/or interestsexpressed by one or more users that may form the basis of a futurequery, and may be used to pre-fetch data for such a future query. Anytype of query may be utilized, such as a search query, an analyticsquery which determines something about the underlying data returned bythe analytics query (e.g., one or more aspects, characteristics,similarities, and/or differences between data in the query).Conventionally, all of the files 504 are duplicated to the local clusterrequesting the query. In another conventional approach, the query may beexecuted on the central cluster 506, thereby using up valuable resourcesof the central cluster 506, rather than pushing the workload out to oneof the local clusters 508.

As shown in FIG. 5C, in response to groups 516 pre-existing on thecentral cluster 506 that are cognitively created to satisfy commonqueries, relevant files to a particular query 514 may be quicklydetermined and duplicated on a local cluster 518 from which the query514 originates, as opposed to duplicating all files 504 to the localcluster 518 from which the query 514 originated. This saves substantialresources over conventional processes, as there is no need to filterresults using resources (such as memory space, processor capacity, etc.)of the central cluster 506 or filtering all the files 504 onceduplicated on the local cluster 518 to determine which of the files 504are relevant to the query. This is due, in part, to resources of thelocal cluster 518 being utilized to only filter a subset of the files504, e.g., those which are within one or more particular groups 520 onthe central cluster 506 which relate to the query, as determined by thecentral cluster 506 upon receiving the query 514.

After filtering and grouping is performed on at least some of the files504 of the central cluster 506, the various filters that are used togroup the files 504 learn and adapt over time, based on the usefulnessof existing groups and the usage patterns of the local clusters 508 forgroups on the central cluster 506. This learning will adjust the filtersso that they are able to provide groups which include files that aremore relevant to received queries, so that less resources of the centralcluster 506 are used in response to queries being received in thefuture. Of course, there is no way to completely anticipate what querieswill be received, but smart, learning filters may be able to providegroups that are able to be used to respond to greater than 90% of allreceived queries, with the remaining queries being responded to withresults after filtering the files 504 on the central cluster 506. Thelearning and adaptation of the filters may cause the various groups 516to be altered and/or modified in response to the changes to the files504, such as by adding new group(s), modifying the files that are withinone or more groups, removing one or more existing groups, etc.

In another embodiment, the individual files that are grouped together inany single group (such as group 520) may be altered and/or modified overtime, in response to the changes, such as by adding one or more newfiles to a group, removing one or more existing files from a group,modifying which groups a particular file belongs, etc.

Any relevant changes may be accounted for in the grouping of individualfiles, and the groups 516 themselves, on the central cluster 506, suchas changes to raw data underlying the filtered results (e.g., raw datachanges may cause a file to no longer be relevant to one group and/ormake the file relevant to a group it has not already been added to), aperformance-based metric which measures grouping success (e.g., howsuccessful the grouping is in saving resources for queries submittedfrom the local clusters 508), popularity of files within a particulargroup (e.g., how often a particular group is duplicated to a localcluster versus a normalized average for all groups), taxonomy changes,etc.

In another embodiment, cache eviction policies may be used to determinewhich groups 516 to maintain on any particular local cluster (or on alllocal clusters 508 in a further embodiment) and which groups to delete(or allow to be written over) to free up space for more frequentlyaccessed or more recently requested information. For example, after agroup (such as group 520) is duplicated to a local cluster (such aslocal cluster 518) in response to a query being executed on the localcluster, the group may be maintained within the local cluster for apredetermined period of time as dictated by a cache eviction policy,such as 1 week, 1 day, 12 hours, 3 hours, etc. In response to the groupbeing utilized for another query (whether all of the files in the groupor some sub-grouping thereof), the pre-determined amount of time may berest for this particular group so that it will remain on the localcluster for an extended period of time (time after first query untilsecond query+predetermined period of time) in contrast to a group whichis only utilized for the first query.

The period of time to maintain a group (such as group 520) on the localcluster (such as local cluster 518), as dictated by one or more cacheeviction policies, may be set to apply to a single local cluster in oneembodiment, a subset of local clusters in another embodiment, orglobally to all local clusters 508, in yet another embodiment. Anadministrator may set the cache eviction policy as seen fit.

In this way, a group (such as group 520) may be re-utilized one or moretimes after an initial query is executed, as long as it remains on thelocal cluster (such as local cluster 518), so that the underlying filesof the group do not need to be re-duplicated to the local cluster eachtime a query dictates access to the files of the group.

In one example, as shown in FIG. 6 for a distributed system 600 thathosts medical data 602, assume that a query 606 is executed on the datastored to the distributed system 600 for medical data that involves“X-Rays.” Also assume that the central cluster 604 has already groupedthe medical data 602 stored therein by medical type, e.g., “X-Rays” 608,“PET-scans” 610, “CT-scans” 612, “Ultrasounds” 614, etc. In response tothe query for “X-Rays,” only the data residing within the group “X-Rays”608 is sent to the local cluster 616 on which the query originated.Thereafter, the resources of the local cluster 616 are utilized toexecute the analytics query on the data residing within the group“X-Rays” 608 instead of the resources of the central cluster 604.Moreover, as time passes since the original query, a cache evictionpolicy may determine a time in which the data residing within the group“X-Rays” 608 is evicted from the local cluster 616, so that the data maybe re-used in additional queries on the local cluster 616.

Now referring to FIG. 7, a method 700 is shown according to oneembodiment. The method 700 may be performed in accordance with thepresent invention in any of the environments depicted in FIGS. 1-6,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 7 may be included in method700, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 700 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 700 may be partially or entirely performed by amicroprocessor, a server, a cluster of computing devices (e.g., acentral cluster), a processing circuit having one or more processorstherein, or some other device comprising one or more processors. Theprocessing circuit, e.g., processor(s), chip(s), and/or module(s)implemented in hardware and/or software, and preferably having at leastone hardware component, may be utilized in any device to perform one ormore steps of the method 700. Illustrative processors include, but arenot limited to, a MPU, a CPU, an ASIC, a FPGA, etc., combinationsthereof, or any other suitable computing device known in the art.

As shown in FIG. 7, method 700 may start with operation 702, where aplurality of files stored to a central cluster of a distributed filesystem are filtered to place independent portions of the plurality offiles into a plurality of groups using filters designed for suchfiltering prior to receiving a query on the plurality of files. In thisfiltering process, files within each of the plurality of groups aregrouped together because they share a common searchable characteristic,such as date, user(s), geographical location, content (e.g., type ofinformation included in the file such as person, place, concept, idea,object, etc., that the file relates to, includes information for,describes, etc.), type (text, audio, video, etc.), size, storagelocation, etc.

In operation 704, an indication of the query is received at the centralcluster. This indication may be the query itself, a file or referencewhich describes the query and/or intended purpose of the query (e.g., atype of the query, targeted files and/or file types for the query,etc.).

In operation 706, the query is responded to by duplicating files of oneor more of the plurality of groups that correspond to the query to alocal cluster of the distributed file system that provided theindication of the query. The local cluster is geographically diversefrom the central cluster, e.g., they are located in differentgeographical locations.

Which files relate to the query may be determined based on keywordgrouping of the files on the central cluster. For example, if the queryis searching for all deeds for houses within a certain zip code, thecentral cluster may return the files from a group that includes allfiles that have deeds therein that have been narrowed by a sub-groupthat includes only those deeds within the requested zip code.

In a further embodiment, method 700 may include receiving the pluralityof files at the central cluster from one or more sources. The sourcesmay include any suitable source of data and files, such as localclusters of the distributed file system, hosts, servers, the Internet, aSAN that provide data to the distributed file system, etc. The pluralityof files include text and unstructured data in some approaches. Theunstructured data may include image data, audio data, proprietaryformatted data files, etc. Moreover, method 700 may include storing theplurality of files to the central cluster and converting theunstructured data into text on the central cluster. In this way, thefilters may be applied to the text of the files after being convertedfrom the unstructured data such that even images, video, audio, etc.,may be grouped into logical sub-containers on the central cluster forprovision to a local cluster in response to a query therefrom.

According to another embodiment, method 700 may include generating thefilters based on one or more factors to create the plurality of groupsthat include files commonly requested in queries received at the centralcluster. The factors may include, but are not limited to: dateassociated with one or more files, one or more users associated with oneor more files, selected content and/or one or more keywords of one ormore files, a geographic location associated with one or more files,etc. Other factors may include keyword(s) searched against the files todetermine similar and/or common content within files that are organizedtogether in a common group. Further, the filters may be adapted overtime to group together files that are commonly requested in queriesreceived at the central cluster. In this way, as more and more queriesare received, the filters may learn the optimum grouping for the filesstored on the central cluster to efficiently respond to the queries. Forexample, when a query for a certain subset of files is received morethan once, a group may be created that includes the subset of files sothat they may be returned for each subsequent query for the same subsetof files. This type of learning and adapting is useful for reducing theresource burden on the central cluster, which would be required tofilter these results each time the query is requested, rather than onlyonce to create the group.

The filters may be manually generated, such as by an administrator ofthe central cluster or some other system that has access to the filters,to improve a likelihood that files within a particular group arerelevant to one or more subsequently received queries. In an alternateembodiment, the filters may be cognitively and intelligently generatedautomatically by the central cluster or some other system that hasaccess to the filters, thereby eliminating or greatly reducing inputfrom users of the system(s) in order to create the filters and maximizethe likelihood that files within particular groups are relevant tosubsequently received queries on the central cluster.

In another embodiment, method 700 may include updating the plurality ofgroups to account for changes to the plurality of files stored to thecentral cluster. As the files change over time, so too will the groupsand membership of files within the groups. Therefore, the updating theplurality of groups may occur periodically based on a predeterminedschedule, continuously as a background operation, or in response to atriggering event. Various different actions may be performed to updatethe plurality of groups, including but not limited to: removing one ormore groups, adding one or more groups, adding one or more files to aparticular group, and removing one or more files from the particulargroup. Any suitable triggering event may be used to cause the groups andmembership therein to be updated. In a further embodiment, thetriggering event may include, but is not limited to: deletion of anexisting file on the central cluster, addition of a new file to thecentral cluster, addition of a new file type to the central cluster, andupdate of a text conversion process of the central cluster.

Method 700 may be implemented in a system and/or a computer programproduct. For example, a system may include a processing circuit andlogic integrated with the processing circuit, executable by theprocessing circuit, or integrated with and executable by the processingcircuit. By integrated with, what is meant is that the processingcircuit is a hardware processor that has hardcoded logic includedtherewith, such as an ASIC, a FPGA, etc. By executable by, what is meantis that the processor is configured to execute software logic to achievefunctionality dictated by the software logic, with the processorpossibly being a MPU, a CPU, a microprocessor, etc. The logic isconfigured to cause the processing circuit to perform method 700.

In another example, a computer program product may include a computerreadable storage medium having program instructions embodied therewith.The computer readable storage medium may be any suitable storage deviceknown in the art that is configured to store and allow computer accessto information stored therein. The embodied program instructions areexecutable by a processing circuit to cause the processing circuit toperform method 700.

Now referring to FIG. 8, a method 800 is shown according to oneembodiment. The method 800 may be performed in accordance with thepresent invention in any of the environments depicted in FIGS. 1-6,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 8 may be included in method800, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 800 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 800 may be partially or entirely performed by amicroprocessor, a server, a cluster of computing devices (e.g., a localcluster), a processing circuit having one or more processors therein, orsome other device comprising one or more processors. The processingcircuit, e.g., processor(s), chip(s), and/or module(s) implemented inhardware and/or software, and preferably having at least one hardwarecomponent, may be utilized in any device to perform one or more steps ofthe method 800. Illustrative processors include, but are not limited to,a MPU, a CPU, an ASIC, a FPGA, etc., combinations thereof, or any othersuitable computing device known in the art.

As shown in FIG. 8, method 800 may start with operation 802, where alocal cluster of a distributed file system is searched or otherwisescanned for files relevant to a query. This operation takes place priorto sending an indication of the query to a central cluster of thedistributed file system to ensure that if the files already exist on thelocal cluster, that interaction with the central cluster is minimized.

Moreover, in a further embodiment, after determining that files relevantto the query exist on the local cluster, a separate query may be sent tothe central cluster to determine whether any of the existing files havebeen updated on the central cluster since being duplicated to the localcluster, and if so, such updated files are duplicated to the localcluster to replace the out-of-date files stored to the local cluster.

In operation 804, the indication of the query is sent to the centralcluster in response to a determination that the relevant files are notstored to the local cluster. In one embodiment, the query itself may besent as the indication. In other embodiments, the indication includespertinent information needed by the central cluster to determine fileswhich would be relevant to the query, such as parameter value(s),date(s), time(s), location(s), user(s), etc.

In operation 806, a group of files relevant to the query are received atthe local cluster. In one embodiment, this group of files are sent bythe central cluster in response to the indication of the query beingsent to and received by the central cluster. This group of files mayalready exist on the central cluster prior to the indication of thequery being sent to the central cluster, in one embodiment, therebyproviding for a fast response to the query and the ability for the localcluster to perform the query on the received group of files rather thanrequiring the central cluster to perform such action.

In operation 808, the query is executed on the group of files, with theproviso that the group of files does not include all files stored to thecentral cluster. Any suitable query may be executed, such as ananalytics query which may include complex functionality and/or multipleanalysis processes within the single query, a search query, an appendquery, a delete query, a make table query, etc.

In operation 810, the group of files are stored on the local cluster fora predetermined period of time starting from a last access of the groupof files in accordance with a cache eviction policy. In this way, shoulda subsequent query be issued by the local cluster that will utilize thesame or an overlapping portion of the group of files, the group of filesdo not need to be duplicated from the central cluster, thereby savingresources of the central cluster and the network in general.

The cache eviction policy may be created and/or adjusted by anadministrator to effectively manage the files that are stored in thelocal cluster to better utilize the limited storage space of the localcluster and maximize resource usage thereof and minimize resource usageof the central cluster.

Method 800 may be implemented in a system and/or a computer programproduct. For example, a system may include a processing circuit andlogic integrated with the processing circuit, executable by theprocessing circuit, or integrated with and executable by the processingcircuit. By integrated with, what is meant is that the processingcircuit is a hardware processor that has hardcoded logic includedtherewith, such as an ASIC, a FPGA, etc. By executable by, what is meantis that the processor is configured to execute software logic to achievefunctionality dictated by the software logic, with the processorpossibly being a MPU, a CPU, a microprocessor, etc. The logic isconfigured to cause the processing circuit to perform method 800.

In another example, a computer program product may include a computerreadable storage medium having program instructions embodied therewith.The computer readable storage medium may be any suitable storage deviceknown in the art that is configured to store and allow computer accessto information stored therein. The embodied program instructions areexecutable by a processing circuit to cause the processing circuit toperform method 800.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an ASIC, a FPGA,etc. By executable by the processor, what is meant is that the logic ishardware logic; software logic such as firmware, part of an operatingsystem, part of an application program; etc., or some combination ofhardware and software logic that is accessible by the processor andconfigured to cause the processor to perform some functionality uponexecution by the processor. Software logic may be stored on local and/orremote memory of any memory type, as known in the art. Any processorknown in the art may be used, such as a software processor module and/ora hardware processor such as an ASIC, a FPGA, a CPU, an integratedcircuit (IC), a graphics processing unit (GPU), etc.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method, comprising: filtering a plurality offiles stored to a central cluster of a distributed file system to placeindependent portions of the plurality of files into a plurality ofgroups using filters prior to receiving a query on the plurality offiles, wherein files within each of the plurality of groups share acommon searchable characteristic; receiving, at the central cluster, anindication of the query; and responding to the query by duplicatingfiles of one or more of the plurality of groups that correspond to thequery to a local cluster of the distributed file system that providedthe indication of the query and is geographically diverse from thecentral cluster.
 2. The method as recited in claim 1, comprising:receiving the plurality of files at the central cluster from one or moresources, the plurality of files including text and unstructured data;storing the plurality of files to the central cluster; and convertingthe unstructured data into text on the central cluster, wherein thefilters are applied to the text of the files after being converted fromthe unstructured data.
 3. The method as recited in claim 1, comprising:generating the filters based on one or more factors to create theplurality of groups that include files commonly requested in queriesreceived at the central cluster, the factors being selected from a setof factors comprising: a date associated with one or more files, one ormore users associated with one or more files, selected content and/orone or more keywords of one or more files, and a geographic locationassociated with one or more files.
 4. The method as recited in claim 3,comprising: adapting the filters over time to group together files thatare commonly requested in queries received at the central cluster. 5.The method as recited in claim 3, wherein the filters are generated tomaximize a likelihood that files within a particular group are relevantto one or more received queries.
 6. The method as recited in claim 1,comprising: updating the plurality of groups to account for changes tothe plurality of files stored to the central cluster, wherein theupdating the plurality of groups occurs periodically based on apredetermined schedule, continuously as a background operation, or inresponse to a triggering event, and wherein the updating the pluralityof groups includes an action selected from a set of actions comprising:removing one or more groups, adding one or more groups, adding one ormore files to a particular group, and removing one or more files fromthe particular group.
 7. The method as recited in claim 6, wherein thetriggering event is selected from a set comprising: deletion of anexisting file on the central cluster, addition of a new file to thecentral cluster, addition of a new file type to the central cluster, andupdate of a text conversion process of the central cluster.
 8. Acomputer program product, the computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, wherein the computer readable storage medium is not atransitory signal per se, the embodied program instructions beingexecutable by a processing circuit to cause the processing circuit to:filter, by the processing circuit, a plurality of files stored to acentral cluster of a distributed file system to place independentportions of the plurality of files into a plurality of groups usingfilters prior to receiving a query on the plurality of files, whereinfiles within each of the plurality of groups share a common searchablecharacteristic; receive, by the processing circuit at the centralcluster, an indication of the query; and respond to the query, by theprocessing circuit, by duplicating files of one or more of the pluralityof groups that correspond to the query to a local cluster of thedistributed file system that provided the indication of the query. 9.The computer program product as recited in claim 8, wherein the embodiedprogram instructions are further executable by the processing circuit tocause the processing circuit to: receive, by the processing circuit, theplurality of files at the central cluster from one or more sources, theplurality of files including text and unstructured data; store, by theprocessing circuit, the plurality of files to the central cluster; andconvert, by the processing circuit, the unstructured data into text onthe central cluster, wherein the filters are applied to the text of thefiles after being converted from the unstructured data.
 10. The computerprogram product as recited in claim 8, wherein the embodied programinstructions are further executable by the processing circuit to causethe processing circuit to: generate, by the processing circuit, thefilters based on one or more factors to create the plurality of groupsthat include files commonly requested in queries received at the centralcluster, the factors being selected from a set of factors comprising: adate associated with one or more files, one or more users associatedwith one or more files, selected content and/or one or more keywords ofone or more files, and a geographic location associated with one or morefiles.
 11. The computer program product as recited in claim 10, whereinthe filters are generated to maximize a likelihood that files within aparticular group are relevant to one or more received queries.
 12. Thecomputer program product as recited in claim 10, wherein the embodiedprogram instructions are further executable by the processing circuit tocause the processing circuit to: adapt, by the processing circuit, thefilters over time to group together files that are commonly requested inqueries received at the central cluster.
 13. The computer programproduct as recited in claim 8, wherein the embodied program instructionsare further executable by the processing circuit to cause the processingcircuit to: update, by the processing circuit, the plurality of groupsto account for changes to the plurality of files stored to the centralcluster, wherein the embodied program instructions that cause theprocessing circuit to update the plurality of groups further cause theprocessing circuit to perform an action selected from a set of actionscomprising: removing one or more groups, adding one or more groups,adding one or more files to a particular group, and removing one or morefiles from the particular group, and wherein the embodied programinstructions that cause the processing circuit to update the pluralityof groups are executed periodically based on a predetermined schedule,continuously as a background operation, or in response to a triggeringevent.
 14. The computer program product as recited in claim 13, whereinthe triggering event is selected from a set comprising: deletion of anexisting file on the central cluster, addition of a new file to thecentral cluster, addition of a new file type to the central cluster, andupdate of a text conversion process of the central cluster.
 15. Asystem, comprising: a processing circuit; a memory; and logic stored tothe memory, that when executed by the processing circuit causes theprocessing circuit to: filter a plurality of files stored to a centralcluster of a distributed file system to place independent portions ofthe plurality of files into a plurality of groups using filters prior toreceiving a query on the plurality of files, wherein files within eachof the plurality of groups share a common searchable characteristic;receive, at the central cluster, an indication of the query; and respondto the query by duplicating files of one or more of the plurality ofgroups that correspond to the query to a local cluster of thedistributed file system that provided the indication of the query. 16.The system as recited in claim 15, wherein the logic further causes theprocessing circuit to: receive the plurality of files at the centralcluster from one or more sources, the plurality of files including textand unstructured data; store the plurality of files to the centralcluster; and convert the unstructured data into text on the centralcluster, wherein the filters are applied to the text of the files afterbeing converted from the unstructured data.
 17. The system as recited inclaim 15, wherein the logic further causes the processing circuit to:generate the filters based on one or more factors to create theplurality of groups that include files commonly requested in queriesreceived at the central cluster, the factors being selected from a setof factors comprising: a date associated with one or more files, one ormore users associated with one or more files, selected content and/orone or more keywords of one or more files, and a geographic locationassociated with one or more files.
 18. The system as recited in claim17, wherein the filters are generated to maximize a likelihood thatfiles within a particular group are relevant to one or more receivedqueries.
 19. The system as recited in claim 17, wherein the logicfurther causes the processing circuit to: adapt the filters over time togroup together files that are commonly requested in queries received atthe central cluster.
 20. The system as recited in claim 15, wherein thelogic further causes the processing circuit to: update the plurality ofgroups to account for changes to the plurality of files stored to thecentral cluster periodically based on a predetermined schedule,continuously as a background operation, or in response to a triggeringevent, and wherein the logic that causes the processing circuit toupdate the plurality of groups performs an action selected from a set ofactions comprising: removing one or more groups, adding one or moregroups, adding one or more files to a particular group, and removing oneor more files from the particular group.
 21. The system as recited inclaim 20, wherein the triggering event is selected from a setcomprising: deletion of an existing file on the central cluster,addition of a new file to the central cluster, addition of a new filetype to the central cluster, and update of a text conversion process ofthe central cluster.
 22. A method, comprising: receiving a plurality offiles at a central cluster of a distributed file system from one or moresources, the plurality of files including text and unstructured data;storing the plurality of files to the central cluster; converting theunstructured data into text on the central cluster; and filtering theplurality of files to place independent portions of the plurality offiles into a plurality of groups using filters prior to receiving aquery on the plurality of files, wherein files within each of theplurality of groups share a common searchable characteristic, andwherein the filters are applied to the text of the files after beingconverted from the unstructured data.
 23. The method as recited in claim22, comprising: receiving, at the central cluster, an indication of thequery; responding to the query by duplicating files of one or more ofthe plurality of groups that correspond to the query to a local clusterof the distributed file system that provided the indication of thequery; and adapting the filters over time to group together files thatare commonly requested in queries received at the central cluster. 24.The method as recited in claim 22, comprising: generating the filtersbased on one or more factors to create the plurality of groups thatinclude files commonly requested in queries received at the centralcluster, the factors being selected from a set of factors comprising: adate associated with one or more files, one or more users associatedwith one or more files, selected content and/or one or more keywords ofone or more files, and a geographic location associated with one or morefiles; and adapting the filters over time to group together files thatare commonly requested in queries received at the central cluster.
 25. Amethod, comprising: searching a local cluster of a distributed filesystem for files relevant to a query prior to sending an indication ofthe query to a central cluster of the distributed file system; sendingthe indication of the query to the central cluster in response to adetermination that the relevant files are not stored to the localcluster; receiving, at the local cluster, a group of files relevant tothe query; executing the query on the group of files, with the provisothat the group of files does not include all files stored to the centralcluster; and storing the group of files on the local cluster for apredetermined period of time starting from a last access of the group offiles in accordance with a cache eviction policy.